论文标题
学员网络：共同学习CAD操作类型和边界代理的步骤
CADOps-Net: Jointly Learning CAD Operation Types and Steps from Boundary-Representations
论文作者
论文摘要
3D逆向工程是一个备受追捧的人，但在计算机辅助设计（CAD）行业中却没有完全实现的目标。目的是恢复CAD模型的施工历史。从CAD模型的边界表示（B-REP）开始，本文提出了一个新的深神经网络CADOPS-NET，该网络共同学习了CAD操作类型，并将分解分解为不同的CAD操作步骤。这种联合学习允许将B-REP分为由各种CAD操作在同一施工步骤中创建的部分；因此提供相关信息以进一步恢复设计历史记录。此外，我们提出了新颖的CC3D-OPS数据集，其中包括带有CAD操作类型标签和步骤标签的$ 37K $ CAD型号。与现有数据集相比，CC3D-OPS模型的复杂性和种类更接近用于工业目的的模型。我们对拟议的CC3D-OPS和公开融合360数据集进行的实验证明了Cadops-NET相对于最先进的竞争性能，并确认了CAD操作类型和步骤的联合学习的重要性。
3D reverse engineering is a long sought-after, yet not completely achieved goal in the Computer-Aided Design (CAD) industry. The objective is to recover the construction history of a CAD model. Starting from a Boundary Representation (B-Rep) of a CAD model, this paper proposes a new deep neural network, CADOps-Net, that jointly learns the CAD operation types and the decomposition into different CAD operation steps. This joint learning allows to divide a B-Rep into parts that were created by various types of CAD operations at the same construction step; therefore providing relevant information for further recovery of the design history. Furthermore, we propose the novel CC3D-Ops dataset that includes over $37k$ CAD models annotated with CAD operation type labels and step labels. Compared to existing datasets, the complexity and variety of CC3D-Ops models are closer to those used for industrial purposes. Our experiments, conducted on the proposed CC3D-Ops and the publicly available Fusion360 datasets, demonstrate the competitive performance of CADOps-Net with respect to state-of-the-art, and confirm the importance of the joint learning of CAD operation types and steps.
